Probabilistic Constrained MPC for Multiplicative and Additive Stochastic Uncertainty

Author

Cannon, Mark ; Kouvaritakis, Basil ; Wu, Xingjian

Author_Institution

Dept. of Eng. Sci., Univ. of Oxford, Oxford, UK

Volume

54

Issue

7

fYear

2009

fDate

7/1/2009 12:00:00 AM

Firstpage

1626

Lastpage

1632

Abstract

The technical note develops a receding horizon control strategy to guarantee closed-loop convergence and feasibility in respect of soft constraints. Earlier results addressed the case of multiplicative uncertainty only. The present technical note extends these to the more general case of additive and multiplicative uncertainty and proposes a method of handling probabilistic constraints. The results are illustrated by a simple design study considering the control of a wind turbine.

Keywords

closed loop systems; machine control; predictive control; stochastic processes; stochastic systems; uncertain systems; wind turbines; additive stochastic uncertainty; closed-loop convergence; model predictive control; multiplicative stochastic uncertainty; probabilistic constrained MPC; probabilistic constraints; receding horizon control; soft constraints; wind turbine control; Automatic control; Constraint optimization; Convergence; Cost function; Equations; Fatigue; Motion control; Optimal control; Predictive control; Predictive models; Robust control; Robustness; Stochastic processes; Tracking; Trajectory; Uncertainty; Underwater vehicles; Wind turbines; Constrained control; fatigue life; optimization; stochastic control;

fLanguage

English

Journal_Title

Automatic Control, IEEE Transactions on

Publisher

ieee

ISSN

0018-9286

Type

jour

DOI

10.1109/TAC.2009.2017970

Filename

5071166